2 Computer Vision Engineer Resume Examples & Writing Guide

Creating an effective computer vision engineer resume is critical for landing the job you want. This guide provides 2 complete resume samples and a step-by-step walkthrough for writing every section - summary, experience, skills, and education. Apply these examples and tips to build a resume that grabs hiring managers' attention and positions you for success.

Writing a great resume is very important if you want to get hired as a Computer Vision Engineer. Many people will apply for each open position. Having a resume that grabs attention and shows off your skills can help you rise above the rest.

But what does a winning Computer Vision resume look like? How can you make your background and abilities shine? In this article, we'll break down the key parts every resume needs. We'll share two real examples from Computer Vision Engineers who got hired at top companies.

By the end, you'll know exactly how to put together a resume that gets you the interview. Even if English isn't your first language, our tips will be easy to understand and follow. Let's dive in and learn how to create a Computer Vision Engineer resume that no hiring manager can ignore!

Common Responsibilities Listed on Computer Vision Engineer Resumes

  • Developing and implementing computer vision algorithms and models for various applications such as object detection, image recognition, and image segmentation
  • Optimizing computer vision models for performance, accuracy, and efficiency
  • Collaborating with cross-functional teams, including data scientists, software engineers, and product managers, to integrate computer vision solutions into products and services
  • Conducting research and keeping up-to-date with the latest advancements in computer vision techniques, algorithms, and tools
  • Designing and implementing data collection, preprocessing, and augmentation pipelines for training computer vision models
  • Analyzing and interpreting results from computer vision models, identifying areas for improvement, and iterating on solutions
  • Developing and maintaining computer vision software libraries, tools, and APIs
  • Ensuring the reliability, scalability, and maintainability of computer vision systems
  • Presenting research findings, technical solutions, and project updates to stakeholders and technical teams

How to write a Resume Summary

Nurturing the Importance of a Robust Summary/Objective Section

We live in an increasingly busy world, where time is often the most valuable commodity. Hiring Managers and Recruiters are no exception to this rule, hence it becomes exceedingly important to craft an impactful summary/objective section on your resume. Essentially, it serves as an elevator pitch, providing a snapshot of your capabilities and experience as a Computer Vision Engineer.

Focused and concise are two adjectives we stand for in generating this section, without making any attenuation in impact. The goal is to encapsulate the essence of your skills, work history, and professional objectives in just a few sentences, all the while maintaining straightforward language.

By displaying your important qualities, specializations and goals up front, you become instantly more intelligible to the recruiter. It also provides an opportunity to emphasize your strengths as a Computer Vision Engineer without their having to delve deeper into your resume.

A good summary/objective begins by framing the breadth and depth of your experience. To say, for instance, “Computer Vision Engineer with substantial experience in…” provides context and demonstrates your professional capacity. This iceberg approach of disclosing only the surface lets you set a theme for the rest of the resume and functions as a springboard for the recruiter to dive deeper into the details.

Next, your specializations or standout skills must take the stage. As a Computer Vision Engineer, you might want to highlight your expertise in areas such as machine learning algorithms, pattern recognition, or image processing - depending on the specifics of the role you're applying for. This introduces you as an expert and gives an idea of what you can bring to the table.

The conclusion we don't precisely formalize, but it's still vital to point towards your professional future here. Including your career objectives and how you hope to contribute to the prospective employer instills a sense of passion and purpose in your summary. It aligns your individual goals with those of the company and reflects your interest in collective growth.

Crafting the summary/objective section does seem like a daunting task – boiling down years of hard work into few sentences isn't easy. Yet, never underestimate the impact of those few lines connecting the dots between an opportunity and your own qualifications for it. Fluent, concise, and with a spark of individuality: This is the recipe for a summary that carries your resume above and beyond the multitude of others on a recruiter's desk.

Remember! Meaning, not marketing. Value, not verbosity. Here tips the true balance in your favor.

Imagery Section Here, you would see practical imagery examples. However, as per your request, no examples have been included.

Strike a balance, tread the line between modesty and confidence, and you are on your journey to a compelling and truthful portrayal of oneself. Good Luck!

Strong Summaries

  • A meticulous and innovative computer vision engineer with 6 years of experience in designing and implementing computer vision applications. Proven ability in developing algorithms, system design and image processing.
  • An analytical Computer Vision Engineer with a strong background in Mathematics, holding a PhD in Computer Vision. 8 years of experience in deep learning models used for Image classification, object detection and semantic segmentation.
  • Results-driven computer vision engineer with over 10 years’ experience in the field. Specialized in artificial intelligence and proficient in modern programming languages, data analysis, algorithm development and machine learning.
  • Detailed-oriented computer vision engineer with 4+ years of experience in building and implementing machine learning models. Excellent problem-solving abilities and hands-on experience in Python, TensorFlow, and OpenCV.

Why these are strong?

These summaries are really good because they not only present the candidates as experienced professionals with specific skills in the field of computer vision, but also offer insights about their particular areas of expertise, such as artificial intelligence, algorithm development, and machine learning. In addition, mentioning the number of years in the field gives proof for expertise. Conjunctively, willing to provide solutions and being result driven, shows positive attributes for a problem-solving engineer role. Furthermore, mentioning proficiency in specific computer vision tools and languages (Python, TensorFlow, OpenCV), is a best practice because it directly addresses required skills.

Weak Summaries

  • I helped my uncle to take nice photos of his shop items for selling online. I know how to use Photoshop as well.
  • As a video games fan, I can definitely understand something about computer vision. I am no stranger to technical stuff because I can install various types of software on my computer.
  • I have always had an eye for art and visual illusions. I used to participate in drawing competitions during the school days and often won.

Why these are weak?

The above are bad examples for a Professional Summary section for a Computer Vision Engineer resume for a number of reasons. Firstly, all these summaries do not provide tangible or professional experiences related to the field of Computer Vision Engineering. A professional summary should highlight professional competencies, experiences, and achievements. The first example talks about casual experience of photography and Photoshop which does not equate to understanding complex computer vision algorithms and systems. The second example is entirely irrelevant as playing video games does not demonstrate any relevant skills or expertise in the field. The third example brings up artistic activities from school days which, despite being related with vision and visual elements, aren't directly relevant to the practices and technical understanding needed in a Computer Vision Engineer role.

Showcase your Work Experience

Welcome to your guide on devising an exceptional Work Experience section for your resume as a Computer Vision Engineer. Never bypass the significance of this segment. It’s your professional history distilled, the chance to show, categorically and clearly, what you managed to achieve throughout your career. Here, we shed light on the key components and skills that make this portion impactful. Easy readability, fleshed out descriptions, and an encircling emphasis on transferable skills are just some of the salient factors to consider.

Preparing the Canvas

Begin with preserving chronological order. Displaying your current role first and subsequently proceeding backward provides a semblance of unity that is easy for prospective employers to grasp. Identify the most meaningful positions you’ve held and the ones that align most closely with the role you're pursuing.

The Backbone: Job Titles and Companies

Each entry in this segment should include the name of the company, the job title you held, and the dates of employment. Make certain to use official titles and to add any specifics that clarify your role. For example, if you were a Computer Vision Engineer in a medical twist, it's necessary to specify that.

Expert Tip

Quantify your achievements and impact using concrete numbers, metrics, and percentages to demonstrate the value you brought to your previous roles.

Adding on the Meat: Descriptions and Achievements

How you describe your roles and successes is pivotal. Be specific, and showcase clarity in terms of roles, tasks, accomplishments, and the technology tools utilized. It's not about merely enumerating tasks - it's about highlighting the value, you as a professional, brought to every position. Use action verbs and precise, quantifiable measures whenever possible. In avoiding generalization, ensure that the descriptions are well-clarified and straight to the point.

Transferable Skills

As a Computer Vision Engineer, your technical proficiency is vital. Still, don't forget soft skills and transferable competencies. From problem-solving to communication, or from teamwork to project management, these attributes carry weight across all jobs and sectors and amplify your professional persona.

Tailoring your Entries

Always adjust your Work Experience section to match the specifics of the position you're applying for. Give priority to experiences that illuminate skills and achievements that are most pertinent to the advertised role. Let every word serve a clear purpose.

In due course, your Work Experience section will metamorphose into an engrossing narrative that depicts your professional journey in the most accessible yet effective way. However, be sure to review and optimize it continuously, aligning it with your career objectives and expanding professional experiences.

Strong Experiences

  • Implemented computer vision methods to detect, recognize, and track objects with 95% accuracy.
  • Collaborated with a team of engineers to design and prototype embedded vision systems for commercial use.
  • Published 3 papers on computer vision methods and their practical applications in a renowned technology journal.
  • Developed an innovative method for 3D reconstruction that improved the performance of existing algorithms by 30%.
  • Managed a project to apply machine learning techniques to improve image classification systems.

Why these are strong?

These examples aptly highlight the candidate's diverse skills, outlining progress in key areas like object recognition, embedded systems, academic research, 3D reconstruction, and image classification. They successfully depict the candidate as a skilled and focused professional with valuable experience in the field of computer vision. This includes both individual technical achievements like implementing algorithms and conducting research, and teamwork capabilities which are relevant to corporate environments. Also, the quantification of achievements helps to vividly demonstrate the impact.

Weak Experiences

  • Worked on some computer vision stuff
  • Was part of a computer vision team
  • Tasks included dealing with computer vision
  • Used open source software
  • Responsible for computer science tasks

Why these are weak?

These examples are vague, generic and do not provide the reader with a clear understanding of what the person's role, contributions or accomplishments were in their previous jobs. It's crucial that bullet points in a resume are specific, measurable, and show impact. 'Worked on some computer vision stuff' lacks specificity about the particular project or tasks carried out. The usage of terms like 'Stuff' and 'Tasks' makes it sound unprofessional. Similarly, 'Was part of a computer vision team' doesn't specify the individual's role, tasks and accomplishments in that team. 'Used open source software' is too broad and it doesn't speak to what problems were solved using the software. Overall, these examples do not create a strong impression about the individual's skills, expertise and contributions in the field of Computer Vision.

Skills, Keywords & ATS Tips

Writing a resume can feel challenging, but understanding key elements can help you present yourself in the best light. Specifically, for a Computer Vision Engineer, understanding the balance and importance of hard and soft skills, along with the role of keywords in beating the Applicant Tracking System (ATS) becomes critical.

Hard Skills

Hard skills refer to the specific technical abilities gained through education, training and experience. In the context of a Computer Vision Engineer, hard skills might include knowledge of programming languages such as Python or C++, the ability to work with machine learning frameworks like TensorFlow, and application of computer vision techniques such as image recognition or 3D model reconstruction. These skills are concrete and easily measurable.

Soft Skills

On the other hand, soft skills are interpersonal or people skills that complement your hard skills. For a Computer Vision Engineer, soft skills may include strong problem-solving skills, teamwork, creativity and adaptability. These skills are less tangible than hard skills, but they're equally important as they enhance your ability to work well with others and adapt to new situations or challenges.

Keywords and ATS

When submitting your resume online, there's a high chance an ATS will scan it before it reaches a human eye. ATS, or Applicant Tracking Systems, look for specific keywords and phrases that match the job description. This helps employers filter out unsuitable candidates. For a Computer Vision Engineer, these could be specific hard and soft skills, programming languages, processes or technologies tied to the role. Including the right keywords in your resume increases your chance of passing the ATS and moving onto the next stage of the hiring process.

Connection of Keywords, ATS & Skills

The main connection between keywords, ATS and the skills you list on your resume is the matching process. The ATS associates keywords in the job description with those in your CV. Hence, ensuring that the hard and soft skills you mention in your resume are exactly the ones highlighted in the job posting can greatly increase your chances of passing the ATS. This highlights the importance of tailoring your resume for each specific job application, making sure to match the skills listed in the job ad with those on your resume.

Remember, your resume’s skills section is not just a list. It’s where you prove you have what the employer needs. Both hard and soft skills should be blended throughout your resume, along with the right keywords to pass ATS, maximizing the chances of being called in for an interview. It’s all about presenting yourself as the best suited candidate for the role, capable of adding value to the organization with your unique mix of skills and abilities.

Top Hard & Soft Skills for Full Stack Developers

Hard Skills

  • Image Processing
  • Machine Learning
  • Deep Learning
  • Computer Vision Algorithms
  • Neural Networks
  • Python Programming
  • OpenCV
  • TensorFlow
  • PyTorch
  • Pattern Recognition
  • Feature Extraction
  • Convolutional Neural Networks
  • Image Segmentation
  • Object Detection
  • Image Classification
  • Soft Skills

  • Problem-Solving
  • Critical Thinking
  • Attention to Detail
  • Analytical Skills
  • Creativity
  • Collaboration
  • Communication
  • Adaptability
  • Time Management
  • Teamwork
  • Innovation
  • Self-Motivation
  • Leadership
  • Interpersonal Skills
  • Presentation Skills
  • Top Action Verbs

    Use action verbs to highlight achievements and responsibilities on your resume.

  • Developed
  • Implemented
  • Optimized
  • Designed
  • Tested
  • Evaluated
  • Deployed
  • Analyzed
  • Trained
  • Validated
  • Enhanced
  • Integrated
  • Researched
  • Collaborated
  • Solved
  • Recognized
  • Improved
  • Generated
  • Utilized
  • Investigated
  • Documented
  • Presented
  • Communicated
  • Managed
  • Coordinated
  • Led
  • Innovated
  • Adapted
  • Prioritized
  • Facilitated
  • Motivated
  • Inspired
  • Negotiated
  • Supported
  • Guided
  • Evaluated
  • Championed
  • Education & Certifications

    To add your education and certificates to your resume as a Computer Vision Engineer, start by creating distinct sections for each on your resume. Under 'Education,' list your degrees in reverse chronological order with the degree type, institution's name, and graduation year. Under 'Certifications,' list accredited certificates relevant to the job; include the certification name, issuing organization, and date received. Highlighting relevant coursework or projects under 'Education' can provide further evidence of your qualifications.

    Some of the most important certifications for Computer Vision Engineers

    Specialization covering deep learning techniques for computer vision applications.

    Nanodegree program focusing on computer vision fundamentals and applications.

    Certification program for TensorFlow developers, including computer vision applications.

    Resume FAQs for Computer Vision Engineers


    What is the ideal resume format for a Computer Vision Engineer?


    The most recommended resume format for a Computer Vision Engineer is the reverse-chronological format. This format highlights your work experience and technical skills, which are crucial for this role.


    How long should a Computer Vision Engineer resume be?


    A Computer Vision Engineer resume should typically be one page long for candidates with less than 10 years of experience, and up to two pages for those with more extensive experience.


    What technical skills should be included in a Computer Vision Engineer resume?


    Some essential technical skills to include are programming languages (Python, C++, MATLAB), computer vision libraries (OpenCV, TensorFlow, PyTorch), machine learning algorithms, image processing techniques, and relevant tools or frameworks.


    How can I highlight my computer vision projects on my resume?


    Dedicate a separate section for 'Computer Vision Projects' or 'Relevant Projects' and provide details about the project objectives, techniques used, and quantifiable results or achievements.


    Should I include research papers or publications on my Computer Vision Engineer resume?


    Yes, if you have published research papers or articles related to computer vision, it is highly recommended to include them in a separate section, such as 'Publications' or 'Research Experience'.


    What certifications or online courses are valuable for a Computer Vision Engineer resume?


    Relevant certifications or online courses from reputable providers like Coursera, edX, or Udacity in areas like Computer Vision, Deep Learning, or Machine Learning can strengthen your resume and demonstrate your commitment to continuous learning.

    Computer Vision Engineer Resume Example

    A Computer Vision Engineer designs and develops artificial intelligence systems capable of extracting meaningful insights from digital images and videos. Key responsibilities include building algorithms for tasks like object detection, facial recognition, and scene reconstruction. When writing a resume for this role, highlight technical skills with tools like Python, OpenCV, and TensorFlow. Detail relevant projects showcasing expertise in areas like neural networks and machine learning models. Academic credentials like a degree in Computer Science or related fields should also be prominently featured.

    Gene Meyer
    (613) 570-3973
    Computer Vision Engineer

    Innovative Computer Vision Engineer with a strong background in developing cutting-edge solutions for image processing, object detection, and 3D reconstruction. Proven track record of delivering high-quality results in fast-paced environments, collaborating with cross-functional teams, and driving continuous improvement. Passionate about leveraging state-of-the-art computer vision techniques to solve complex real-world problems.

    Work Experience
    Senior Computer Vision Engineer
    01/2021 - Present
    Amazon Web Services (AWS)
    • Led the development of a deep learning-based object detection system for Amazon Go stores, achieving a 95% accuracy rate and enabling faster checkout experiences.
    • Implemented a real-time 3D reconstruction pipeline using multi-view stereo algorithms, reducing processing time by 40% and enhancing the quality of generated 3D models.
    • Developed a novel image segmentation algorithm using graph-based techniques, resulting in a 25% improvement in accuracy compared to existing methods.
    • Collaborated with cross-functional teams to integrate computer vision solutions into AWS cloud services, such as Amazon Rekognition and Amazon SageMaker.
    • Mentored junior engineers and conducted technical workshops to promote best practices in computer vision and deep learning.
    Computer Vision Engineer
    06/2018 - 12/2020
    • Developed advanced lane detection and tracking algorithms for Waymo's self-driving vehicles, improving the system's robustness in challenging weather conditions.
    • Implemented a multi-sensor fusion framework to combine data from cameras, LiDAR, and radar, enhancing the perception system's accuracy and reliability.
    • Optimized object detection models for real-time inference on embedded systems, reducing latency by 30% without compromising accuracy.
    • Contributed to the development of a large-scale data annotation pipeline, ensuring high-quality labeled data for training computer vision models.
    • Participated in code reviews and provided technical guidance to ensure the maintainability and scalability of the codebase.
    Computer Vision Research Intern
    05/2017 - 08/2017
    Facebook Reality Labs
    • Conducted research on advanced facial recognition techniques for virtual reality applications, resulting in a novel approach that improved recognition accuracy by 20%.
    • Implemented and evaluated various deep learning architectures for facial landmark detection, identifying the most promising models for further development.
    • Collaborated with the engineering team to integrate the developed facial recognition system into Facebook's Oculus platform.
    • Presented research findings at internal conferences and contributed to scientific papers submitted to top-tier computer vision conferences.
    • Participated in hackathons and ideation sessions to explore innovative applications of computer vision in virtual and augmented reality.
  • Computer Vision
  • Deep Learning
  • Machine Learning
  • Image Processing
  • Object Detection
  • 3D Reconstruction
  • Facial Recognition
  • Python
  • C++
  • TensorFlow
  • PyTorch
  • OpenCV
  • CUDA
  • OpenGL
  • ROS
  • Education
    Ph.D. in Computer Science
    09/2014 - 05/2018
    Stanford University, Stanford, CA
    B.S. in Computer Science
    08/2010 - 05/2014
    University of California, Berkeley, Berkeley, CA
    Senior Computer Vision Engineer Resume Example

    A Senior Computer Vision Engineer develops cutting-edge algorithms for image/video analysis. When writing a resume, emphasize strong technical skills like programming, mathematics, statistics, and machine learning. Highlight computer vision experience through relevant projects, showcasing problem-solving abilities and quantifiable achievements. Academic qualifications, publications, and certifications related to computer vision are assets. Clearly present your background to stand out for this specialized role.

    Craig Wade
    (647) 989-4700
    Senior Computer Vision Engineer

    Innovative and highly skilled Senior Computer Vision Engineer with over 8 years of experience in developing cutting-edge solutions for real-world applications. Proven track record of delivering complex projects on time and within budget, leveraging expertise in deep learning, image processing, and 3D reconstruction. Passionate about pushing the boundaries of computer vision technology to drive business growth and create user-centric products.

    Work Experience
    Senior Computer Vision Engineer
    01/2020 - Present
    • Led a team of 5 engineers to develop advanced object detection and tracking algorithms for Amazon Go stores, improving accuracy by 30% and reducing false positives by 45%.
    • Implemented deep learning models for real-time facial recognition in Amazon Rekognition, enhancing security and user experience for millions of customers.
    • Optimized 3D reconstruction pipelines for Amazon Prime Air drones, enabling precise navigation and obstacle avoidance in complex environments.
    • Collaborated with cross-functional teams to integrate computer vision technologies into various Amazon products and services, driving innovation and customer satisfaction.
    • Mentored junior engineers and promoted knowledge sharing through regular tech talks and workshops.
    Computer Vision Engineer
    06/2018 - 12/2019
    • Developed novel algorithms for image segmentation and scene understanding, contributing to the development of Google Lens and improving its accuracy by 25%.
    • Implemented deep learning models for gesture recognition in Google Nest Hub, enabling intuitive and hands-free user interactions.
    • Optimized computer vision pipelines for Google Street View, reducing processing time by 40% and enhancing the quality of 3D reconstructions.
    • Collaborated with UX designers to create visually appealing and user-friendly interfaces for Google's computer vision applications.
    • Conducted research on few-shot learning techniques for object detection, resulting in a patent application and a 20% improvement in model performance.
    Software Engineer - Computer Vision
    01/2016 - 05/2018
    Meta (Facebook)
    • Developed computer vision algorithms for automatic content moderation on Facebook and Instagram, improving the detection of inappropriate images by 35%.
    • Implemented deep learning models for facial recognition and emotion detection, enhancing user experience and engagement on Facebook platforms.
    • Optimized 3D reconstruction pipelines for Oculus VR headsets, improving the accuracy and realism of virtual environments.
    • Collaborated with research scientists to explore novel applications of computer vision in social media, leading to the development of new features and products.
    • Conducted code reviews and provided guidance to junior engineers, fostering a culture of continuous learning and improvement.
  • Deep Learning
  • Computer Vision
  • Image Processing
  • Object Detection
  • Facial Recognition
  • 3D Reconstruction
  • OpenCV
  • TensorFlow
  • PyTorch
  • Python
  • C++
  • Agile Development
  • Project Management
  • Technical Leadership
  • Education
    Ph.D. in Computer Science
    09/2012 - 08/2016
    Stanford University, Stanford, CA
    B.S. in Computer Science
    09/2008 - 05/2012
    University of California, Berkeley, Berkeley, CA